Surface Animations From Time-Varying Volume Data Sets
Sven Linden, Gerd Reis
Visualization, Imaging, and Image Processing (VIIP) IASTED International Conference on Visualization Imaging and Image Processing (VIIP-2008), 8th, September 1-3, Palma de Mallorca, Spain

Abstract:

This paper describes the generation of surface animations from volumetric time-varying biologic tissue data. The volume data represents marker responses acquired using a confocal microscope. In contrast to well known surface-from-points techniques the markers do not represent a sampled surface, but the marker concentration (reflected as high luminance values) sparsely defines surface like strucures. These structures represent features that many researchers are interested in. Since the raw data does not lend very well for analysis it is advantageous to extract these features in a preprocessing step and to present them as time-varying geometric objects. To reach this goal two main problems are solved: Extraction of surfaces from the volume data set and the fusion of these surfaces into an animation by means of a (global) re-parametrization. We show that the results of this work are suited for spatial as well as temporal analysis.

Surface Animations From Time-Varying Volume Data Sets
Sven Linden, Gerd Reis
Visualization, Imaging, and Image Processing (VIIP) IASTED International Conference on Visualization Imaging and Image Processing (VIIP-2008), 8th, September 1-3, Palma de Mallorca, Spain

Abstract:

This paper describes the generation of surface animations from volumetric time-varying biologic tissue data. The volume data represents marker responses acquired using a confocal microscope. In contrast to well known surface-from-points techniques the markers do not represent a sampled surface, but the marker concentration (reflected as high luminance values) sparsely defines surface like strucures. These structures represent features that many researchers are interested in. Since the raw data does not lend very well for analysis it is advantageous to extract these features in a preprocessing step and to present them as time-varying geometric objects. To reach this goal two main problems are solved: Extraction of surfaces from the volume data set and the fusion of these surfaces into an animation by means of a (global) re-parametrization. We show that the results of this work are suited for spatial as well as temporal analysis.